Predicting hematoma expansion in acute spontaneous intracerebral hemorrhage: integrating clinical factors with a multitask deep learning model for non-contrast head CT

PURPOSE

To predict hematoma growth in intracerebral hemorrhage patients by combining clinical findings with non-contrast CT imaging features analyzed through deep learning.

METHODS

Three models were developed to predict hematoma expansion (HE) in 572 patients. We utilized multi-task learning for both hematoma segmentation and prediction of expansion: the Image-to-HE model processed hematoma slices, extracting features and computing a normalized DL score for HE prediction. The Clinical-to-HE model utilized multivariate logistic regression on clinical variables. The Integrated-to-HE model combined image-derived and clinical data. Significant clinical variables were selected using forward selection in logistic regression. The two models incorporating clinical variables were statistically validated.

RESULTS

For hematoma detection, the diagnostic performance of the developed multi-task model was excellent (AUC, 0.99). For expansion prediction, three models were evaluated for predicting HE. The Image-to-HE model achieved an accuracy of 67.3%, sensitivity of 81.0%, specificity of 64.0%, and an AUC of 0.76. The Clinical-to-HE model registered an accuracy of 74.8%, sensitivity of 81.0%, specificity of 73.3%, and an AUC of 0.81. The Integrated-to-HE model, merging both image and clinical data, excelled with an accuracy of 81.3%, sensitivity of 76.2%, specificity of 82.6%, and an AUC of 0.83. The Integrated-to-HE model, aligning closest to the diagonal line and indicating the highest level of calibration, showcases superior performance in predicting HE outcomes among the three models.

CONCLUSION

The integration of clinical findings with non-contrast CT imaging features analyzed through deep learning showed the potential for improving the prediction of HE in acute spontaneous intracerebral hemorrhage patients.

Added prognostic value of 3D deep learning-derived features from preoperative MRI for adult-type diffuse gliomas

BACKGROUND

To investigate the prognostic value of spatial features from whole-brain MRI using a three-dimensional (3D) convolutional neural network for adult-type diffuse gliomas.

METHODS

In a retrospective, multicenter study, 1925 diffuse glioma patients were enrolled from 5 datasets: SNUH (n = 708), UPenn (n = 425), UCSF (n = 500), TCGA (n = 160), and Severance (n = 132). The SNUH and Severance datasets served as external test sets. Precontrast and postcontrast 3D T1-weighted, T2-weighted, and T2-FLAIR images were processed as multichannel 3D images. A 3D-adapted SE-ResNeXt model was trained to predict overall survival. The prognostic value of the deep learning-based prognostic index (DPI), a spatial feature-derived quantitative score, and established prognostic markers were evaluated using Cox regression. Model evaluation was performed using the concordance index (C-index) and Brier score.

RESULTS

The MRI-only median DPI survival prediction model achieved C-indices of 0.709 and 0.677 (BS = 0.142 and 0.215) and survival differences (P < 0.001 and P = 0.002; log-rank test) for the SNUH and Severance datasets, respectively. Multivariate Cox analysis revealed DPI as a significant prognostic factor, independent of clinical and molecular genetic variables: hazard ratio = 0.032 and 0.036 (P < 0.001 and P = 0.004) for the SNUH and Severance datasets, respectively. Multimodal prediction models achieved higher C-indices than models using only clinical and molecular genetic variables: 0.783 vs. 0.774, P = 0.001, SNUH; 0.766 vs. 0.748, P = 0.023, Severance.

CONCLUSIONS

The global morphologic feature derived from 3D CNN models using whole-brain MRI has independent prognostic value for diffuse gliomas. Combining clinical, molecular genetic, and imaging data yields the best performance.

Dynamic Contrast-enhanced MRI Quantification of Altered Vascular Permeability in Autoimmune Encephalitis

Background Blood-brain barrier (BBB) permeability change is a possible pathologic mechanism of autoimmune encephalitis. Purpose To evaluate the change in BBB permeability in patients with autoimmune encephalitis as compared with healthy controls by using dynamic contrast-enhanced (DCE) MRI and to explore its predictive value for treatment response in patients. Materials and Methods This single-center retrospective study included consecutive patients with probable or possible autoimmune encephalitis and healthy controls who underwent DCE MRI between April 2020 and May 2021. Automatic volumetric segmentation was performed on three-dimensional T1-weighted images, and volume transfer constant (Ktrans) values were calculated at encephalitis-associated brain regions. Ktrans values were compared between the patients and controls, with adjustment for age and sex with use of a nonparametric approach. The Wilcoxon rank sum test was performed to compare Ktrans values of the good (improvement in modified Rankin Scale [mRS] score of at least two points or achievement of an mRS score of  ≤2) and poor (improvement in mRS score of less than two points and achievement of an mRS score >2) treatment response groups among the patients. Results Thirty-eight patients with autoimmune encephalitis (median age, 38 years [IQR, 29-59 years]; 20 [53%] female) and 17 controls (median age, 71 years [IQR, 63-77 years]; 12 [71%] female) were included. All brain regions showed higher Ktrans values in patients as compared with controls (P < .001). The median difference in Ktrans between the patients and controls was largest in the right parahippocampal gyrus (25.1 × 10-4 min-1 [95% CI: 17.6, 43.4]). Among patients, the poor treatment response group had higher baseline Ktrans values in both cerebellar cortices (P = .03), the left cerebellar cortex (P = .02), right cerebellar cortex (P = .045), left cerebral cortex (P = .045), and left postcentral gyrus (P = .03) than the good treatment response group. Conclusion DCE MRI demonstrated that BBB permeability was increased in all brain regions in patients with autoimmune encephalitis as compared with controls, and baseline Ktrans values were higher in patients with poor treatment response in the cerebellar cortex, left cerebral cortex, and left postcentral gyrus as compared with the good response group. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Filippi and Rocca in this issue.

Safety, Tolerability, and Pharmacokinetics of a Novel Macrocyclic Gadolinium-Based Contrast Agent, HNP-2006, in Healthy Subjects

OBJECTIVES

Gadolinium-based contrast agents (GBCAs) are indispensable in contrast-enhanced magnetic resonance imaging. A higher risk of gadolinium deposition in linear GBCAs required the introduction of macrocyclic GBCAs with a stable molecular structure. We conducted the first-in-human study to evaluate the safety, tolerability, and pharmacokinetics (PKs) of HNP-2006, a novel macrocyclic GBCA, in healthy male subjects.

MATERIALS AND METHODS

A randomized, placebo-controlled, double-blind, single-ascending dose study was conducted. Subjects received either a single intravenous bolus injection of HNP-2006 or its matching placebo with a treatment-to-placebo ratio of 6:2 at the dose level of 0.02, 0.05, 0.1, 0.2, and 0.3 mmol/kg. Safety was assessed through routine clinical assessments. Blood sampling and urine collection were performed up to 72 hours postdose for PK assessments. Noncompartmental methods were used to calculate PK parameters, and a population PK model was constructed.

RESULTS

Overall, 40 subjects completed the study. Fourteen subjects reported 22 treatment-emergent adverse events (TEAEs). The severity of all TEAEs was mild, and the HNP-2006 dose was associated with the incidence of TEAEs. The most common TEAEs included nausea and dizziness, which occurred within an hour of administration. HNP-2006 was rapidly eliminated by urinary excretion with a half-life of 1.8-2.0 hours and showed a dose-proportional PK. A 2-compartment model had the best fit with the population PK analysis.

CONCLUSIONS

A single intravenous dose of HNP-2006 was well-tolerated and safe up to 0.30 mmol/kg. HNP-2006 was rapidly excreted in urine and exhibited dose-independent PK profiles.

Blood-brain barrier disruption imaging in postoperative cerebral hyperperfusion syndrome using DCE-MRI

Little has been reported about the association between cerebral hyperperfusion syndrome (CHS) and blood-brain barrier (BBB) disruption in human. We aimed to investigate the changes in permeability after bypass surgery in cerebrovascular steno-occlusive diseases using dynamic contrast-enhanced MRI (DCE-MRI) and to demonstrate the association between CHS and BBB disruption. This retrospective study included 36 patients (21 hemispheres in 18 CHS patients and 20 hemispheres in 18 controls) who underwent combined bypass surgery for moyamoya and atherosclerotic steno-occlusive diseases. DCE-MRI and arterial spin labeling perfusion-weighted imaging (ASL-PWI) were obtained at the baseline, postoperative state, and discharge. Perfusion and permeability parameters were calculated at the MCA territory (CBF(territorial), Ktrans(territorial), Vp(territorial)) and focal perianastomotic area (CBF(focal), Ktrans(focal), Vp(focal)) of operated hemispheres. As compared with the baseline, both CBF(territorial) and CBF(focal) increased in the postoperative period and decreased at discharge, corresponding well to symptoms in the CHS group. Vp(focal) was lower in the postoperative period and at discharge, as compared with the baseline. In the control group, no parameters significantly differed among the three points. In conclusion, Vp at the focal perianastomotic area significantly decreased in patients with CHS during the postoperative period. BBB disruption may be implicated in the development of CHS after bypass surgery.

Added value of dynamic contrast-enhanced MR imaging in deep learning-based prediction of local recurrence in grade 4 adult-type diffuse gliomas patients

Local recurrences in patients with grade 4 adult-type diffuse gliomas mostly occur within residual non-enhancing T2 hyperintensity areas after surgical resection. Unfortunately, it is challenging to distinguish non-enhancing tumors from edema in the non-enhancing T2 hyperintensity areas using conventional MRI alone. Quantitative DCE MRI parameters such as Ktrans and Ve convey permeability information of glioblastomas that cannot be provided by conventional MRI. We used the publicly available nnU-Net to train a deep learning model that incorporated both conventional and DCE MRI to detect the subtle difference in vessel leakiness due to neoangiogenesis between the non-recurrence area and the local recurrence area, which contains a higher proportion of high-grade glioma cells. We found that the addition of Ve doubled the sensitivity while nonsignificantly decreasing the specificity for prediction of local recurrence in glioblastomas, which implies that the combined model may result in fewer missed cases of local recurrence. The deep learning model predictive of local recurrence may enable risk-adapted radiotherapy planning in patients with grade 4 adult-type diffuse gliomas.

CT findings of inferior vena cava trauma according to the level of injury: a retrospective analysis of 19 cases in a single trauma centre

AIM

To analyse the clinicoradiological characteristics of traumatic inferior vena cava (IVC) injury level on preoperative computed tomography (CT).

MATERIALS AND METHODS

This retrospective study evaluated patients from a single trauma centre treated for traumatic IVC injury between January 2014 and January 2021. Data on demographics, mechanism of injury, Injury Severity Score, radiological findings on CT and angiography, IVC injury level in surgical findings, complications, and clinical outcomes were collected.

RESULTS

During the 8-year study period, 36 patients presented with traumatic IVC injury: 19 underwent preoperative CT with 17 (89%) blunt and two (11%) penetrating injuries. The most common primary CT sign was contour abnormality (53%, n=10), followed by intraluminal flap and active extravasation (21%, n=4). Among the secondary signs, hepatic laceration (53%, n=10) and retroperitoneal haemorrhage (53%, n=10) were the most common. Frequencies of primary and secondary signs were higher in the infrarenal and suprarenal than in the retrohepatic vena cava injuries. Diagnostic capability of preoperative CT for IVC injury differed according to the IVC level. The detection rate was the highest for an infrarenal vena cava injury at 100% (n=4), followed by that for a suprarenal, suprahepatic, and retrohepatic vena cava injuries at 75% (n=3), 43% (n=3), and 25% (n=1), respectively.

CONCLUSION

CT findings of traumatic IVC injuries may vary depending on the mechanism and anatomical site of injury. Familiarity with IVC injury imaging features may help in diagnosis and surgical treatment planning.

College students' preferences for tobacco treatment: a discrete choice experiment

The purpose of this study was to elicit preferences for the 'format' and 'content' of tobacco treatment among college student smokers, using an online discrete choice experiment (DCE) survey. A DCE survey, supplemented with a think-aloud method, was conducted among 54 college students who smoked combustible cigarettes and/or e-cigarettes. Conditional logistic regression models were constructed to determine optimal profiles of treatment. Cutting down nicotine rather than quitting 'cold turkey' (P < 0.001) and two-way communication (P < 0.001) were viewed as the most critical attributes for the intervention 'format'; changing behaviors rather than social groups/peers (P < 0.001) and autonomy (P < 0.001) were viewed as the most critical attributes for the intervention 'content'. Some preferences varied based on smoking subgroups. Combustible cigarette users preferred interventions with a longer time commitment (P < 0.05) and without nicotine replacement therapies (NRTs) (P < 0.001). Think-aloud data supported the DCE findings and further revealed a strong desire for cutting down nicotine and keeping social groups/peers and misconceptions regarding NRTs. Our study findings can guide tobacco treatment tailored to college students. These treatments should be tailored to specific smoker subgroups.

Deep learning-based reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI

OBJECTIVE

To compare the image quality and diagnostic performance between standard turbo spin-echo MRI and accelerated MRI with deep learning (DL)-based image reconstruction for degenerative lumbar spine diseases.

MATERIALS AND METHODS

Fifty patients who underwent both the standard and accelerated lumbar MRIs at a 1.5-T scanner for degenerative lumbar spine diseases were prospectively enrolled. DL reconstruction algorithm generated coarse (DL_coarse) and fine (DL_fine) images from the accelerated protocol. Image quality was quantitatively assessed in terms of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) and qualitatively assessed using five-point visual scoring systems. The sensitivity and specificity of four radiologists for the diagnosis of degenerative diseases in both protocols were compared.

RESULTS

The accelerated protocol reduced the average MRI acquisition time by 32.3% as compared to the standard protocol. As compared with standard images, DL_coarse and DL_fine showed significantly higher SNRs on T1-weighted images (T1WI; both p < .001) and T2-weighted images (T2WI; p = .002 and p < 0.001), higher CNRs on T1WI (both p < 0.001), and similar CNRs on T2WI (p = .49 and p = .27). The average radiologist assessment of overall image quality for DL_coarse and DL_fine was higher on sagittal T1WI (p = .04 and p < .001) and axial T2WI (p = .006 and p = .01) and similar on sagittal T2WI (p = .90 and p = .91). Both DL_coarse and DL_fine had better image quality of cauda equina and paraspinal muscles on axial T2WI (both p = .04 for cauda equina; p = .008 and p = .002 for paraspinal muscles). Differences in sensitivity and specificity for the detection of central canal stenosis and neural foraminal stenosis between standard and DL-reconstructed images were all statistically nonsignificant (p ≥ 0.05).

CONCLUSION

DL-based protocol reduced MRI acquisition time without degrading image quality and diagnostic performance of readers for degenerative lumbar spine diseases.

CLINICAL RELEVANCE STATEMENT

The deep learning (DL)-based reconstruction algorithm may be used to further accelerate spine MRI imaging to reduce patient discomfort and increase the cost efficiency of spine MRI imaging.

KEY POINTS

• By using deep learning (DL)-based reconstruction algorithm in combination with the accelerated MRI protocol, the average acquisition time was reduced by 32.3% as compared with the standard protocol. • DL-reconstructed images had similar or better quantitative/qualitative overall image quality and similar or better image quality for the delineation of most individual anatomical structures. • The average radiologist's sensitivity and specificity for the detection of major degenerative lumbar spine diseases, including central canal stenosis, neural foraminal stenosis, and disc herniation, on standard and DL-reconstructed images, were similar.

Co-Delivery of Metabolic Modulators Leads to Simultaneous Lactate Metabolism Inhibition and Intracellular Acidification for Synergistic Cancer Therapy

Simultaneous lactate metabolism inhibition and intracellular acidification (LIIA) is a promising approach for inducing tumor regression by depleting ATP. However, given the limited efficacy of individual metabolic modulators, a combination of various modulators is required for highly efficient LIIA. Herein, a co-delivery system that combines lactate transporter inhibitor, glucose oxidase, and O2 -evolving nanoparticles is proposed. As a vehicle, a facile room-temperature synthetic method for large-pore mesoporous silica nanoparticles (L-MSNs) is developed. O2 -evolving nanoparticles are then conjugated onto L-MSNs, followed by immobilizing the lactate transporter inhibitor and glucose oxidase inside the pores of L-MSNs. To load the lactate transporter inhibitor, which is too small to be directly loaded into the large pores, it is encapsulated in albumin by controlling the albumin conformation before being loaded into L-MSNs. Notably, inhibiting lactate efflux shifts the glucose consumption mechanism from lactate metabolism to glucose oxidase reaction, which eliminates glucose and produces acid. This leads to synergistic LIIA and subsequent ATP depletion in cancer cells. Consequently, L-MSN-based co-delivery of modulators for LIIA shows high anticancer efficacy in several mouse tumor models without toxicity in normal tissues. This study provides new insights into co-delivery of small-molecule drugs, proteins, and nanoparticles for synergistic metabolic modulation in tumors.

Distortion correction using topup algorithm by single k-space (TASK) for echo planar imaging

Distortion of echo planar imaging (EPI) can be corrected using B0 field maps, which can be estimated with the topup algorithm that requires two EPI images with opposite distortions. In this study, we propose a new algorithm, termed topup algorithm by single K-space (TASK), to generate two input images from a single k-space for the topup algorithm to correct EPI distortions. The centric EPI contains the opposite phase-encoding polarities in one k-space, which can be divided into two halves with opposite distortions. Therefore, two inputs could be extracted by dividing the k-space into halves and processing them using the proposed procedure including an iterative procedure of automatic brain masking and uniformity correction. The efficiency of TASK was evaluated using 3D EPI. Quantitative evaluations showed that TASK corrected EPI distortion at a similar level to the traditional methods. The estimated field maps from the conventional topup and TASK showed a high correlation ([Formula: see text]). An ablation study showed the validity of every suggested step. Furthermore, it was confirmed that TASK was effective for distortion correction of two-shot centric EPI as well, demonstrating its wider applicability. In conclusion, TASK can correct EPI distortions by its own single k-space information with no additional scan.

Neuroepithelial tumor with EWSR1::PATZ1 fusion: A literature review

We present the clinicopathological and molecular genetic characteristics of a neuroepithelial tumor (NET), EWSR1::PATZ1 fusion-positive with a literature review. This fusion has recently been discovered in rare central nervous system tumors and soft tissue sarcomas and was not included in the fifth edition of the WHO classifications. We identified this fusion in 2 NETs. The first case involved a 7-year-old girl and the second case occurred in a 53-year-old man; both presented with headaches and vomiting. The pediatric case initially showed an intermediate grade of the tumor, but upon recurrences, it transformed into a high-grade tumor with 2 relapses in 8.3 years. This case exhibited high mitotic activity (20/10 high-power fields), and a high Ki-67 index (21%). The TERT promoter (TERTp) mutation was present in both initial and recurrent tumors. In contrast, the adult case was a low-grade tumor with no mitotic activity or recurrence over 13.5 months after subtotal resection and gamma knife surgery. Interestingly, the pediatric case demonstrated a longer survival time compared to conventional glioblastoma. The TERTp mutation, similar to being a molecular signature in adult-type glioblastoma, could also be an indicator of high-grade behavior in PATZ1 fusion NET.

The Role of Local Prostate and Metastasis-Directed Radiotherapy in the Treatment of Oligometastatic Prostate Cancer

PURPOSE/OBJECTIVE(S)

The local ablative therapy for oligometastatic disease (OMD) has the potential to delay further metastases and improve survival. However, it has not been fully elucidated how prostate primary radiotherapy (PPR) and metastasis-directed radiotherapy (MDR) affect prognosis in each different OMD scenario. Herein, we tried to provide efficacy and future perspectives for MDR in oligometastatic prostate cancer.

MATERIALS/METHODS

Patients diagnosed with prostate cancer between 2010 and 2019 and treated for OMD (≤5 active lesions), which occurred synchronously or metachronously, were included. All patients received MDR at all detected lesions (OMDRT). OMDRT which was performed as soon as OMD was detected was classified as early, and OMDRT for progressions after hormone therapy was classified as late. The primary endpoint was survival after OMDRT, and timing of progression after RT was also analyzed.

RESULTS

A total of 82 patients with oligometastatic prostate cancer received OMDRT. Among 36 patients with synchronous OMD, 58% received PPR at diagnosis, and 64% received early OMDRT. Among 46 patients with metachronous OMD, 80% received early OMDRT, and 28 received sequential OMDRT for repetitive OMD events. With a median follow-up of 32 months after OMDRT, 54 patients experienced progression and 5-year survival was 78%. Survival was highest in patients with synchronous OMD and early RT (5-year 86%), and 5-year survival of patients with metachronous OMD and early RT was significantly higher than those with late RT (78% vs. 44%, p = 0.003). Survival of patients with synchronous OMD and PPR was significantly higher than those without PPR or with metachronous OMD (5-year 90% vs. 66%, p = 0.030), by delaying progressions (17.9 vs. 7.0 months, p = 0.005).

CONCLUSION

Survival gain could be achieved through OMDRT in oligometastatic prostate cancer, especially in synchronous OMD status. Also, it was possible to improve the prognosis further when OMDRT was performed early and with PPR.

Patient-Specific Deep Learning Model for Clinical Target Volume Delineation on Daily CBCT of Breast Cancer Patients based on Intentional Deep Overfit Learning (IDOL) Framework

PURPOSE/OBJECTIVE(S)

Increasingly complex target volumes and the use of modern irradiation techniques emphasize the importance of daily image guidance more than ever. Significant progress has been made in adjuvant breast cancer radiotherapy (RT) and the need for optimized image guidance is growing. Furthermore, the position of the breast during RT after breast-conserving surgery is highly variable than expected. In this context, cone beam computed tomography (CBCT) is a very effective tool enabling prompt and accurate adaptive radiation therapy (ART). In this study, we aim to develop a deep learning (DL)-based algorithm to segment clinical target volume (CTV) from daily CBCT scans. Also, we validate the optimization of further learning when applying the Intentional Deep Overfit Learning (IDOL) framework.

MATERIALS/METHODS

A total of 240 different CBCT scans obtained from 100 breast cancer patients were used for this study. CTV was defined as whole breast plus margin in all patients. The workflow consists of two training stages: (1) training a novel 'generalized' DL model (Swin_UNETR) to identify and delineate breast CTV on CBCT scans using 90 breast cancer patient cases (2) applying an 'intentional overfitting' to the 'generalized' DL model to generate a 'patient-specific' model using the remaining 10 breast cancer patients. In this study, for the intentionally overfitting stage, we additionally trained with CBCT scans from the patient's 1st fraction to the 14th fractions cases. The results of the proposed method were compared quantitatively with the expert's contours on 1st-15th fractions CBCT scans using Dice Similarity Coefficient (DSC).

RESULTS

The average DSC between the 'generalized' DL model-based breast CTV contours and reference contours for the patient's 15th fraction was 0.9672. When implementing the IDOL framework with the CBCT scan obtained during the patient's 1st treatment, the average DSC was improved to 0.9809. When additional CBCT scans taken during each of the 1st to 6th fractions were used for training, the average DSC could be most effectively raised to 0.9835. The p-value comparison between the 'generalized' DL model and the 1st fraction was found to be 3.62E-04, while the comparison with the 6th fractions resulted in a p-value of 8.36E-05. The average time required for IDOL training using one CBCT scan and six CBCT scans was 107 seconds and 127 seconds, respectively.

CONCLUSION

In this study, we developed a patient-specific DL-based training algorithm to segment CTV in CBCT scans for breast cancer patients. The performance improvement was relatively significant and was confirmed that using continual DL with additional CBCT scans, which are taken every day, can be more accurate and efficient than drawing breast CTV using a general model. Our novel patient-specific model can be effectively applied to various ARTs by not only reducing labor and time but also increasing accuracy.

Comparison of Early Outcomes of Stereotactic Accelerated Partial Breast Irradiation vs. Volumetric Modulated Arc Therapy-Based FAST-FORWARD Whole Breast Irradiation for Breast Cancer

PURPOSE/OBJECTIVE(S)

Despite evidence supporting APBI from 8 published prospective randomized trials enrolling over 10,000 women, the uptake of APBI in clinical practice is surprisingly low. This is being exacerbated by a new, convenient, and safe shortened WBI schedule. Here, we report the dosimetric and early outcome analyses of the first >1000 patients treated at our institution since the first adoption of stereotactic APBI and the ultra-hypofractionated WBI regimen.

MATERIALS/METHODS

From 2016 to 2022, 801 women with breast cancers in the suitable or cautionary categories according to the ASTRO APBI consensus panel guidelines, received 30 Gy in 5 fractions (92%) either using a robotic stereotactic radiation system (83%) or stereotactic volumetric-based arc therapy (VMAT, 17%). Between 2020 and 2022, 468 women, who were not candidates for APBI and not undergoing any regional irradiation received 26 Gy in 5 fractions using VMAT to the whole breast with the addition of cardiac sparing technique in left-sided breast cancer patients. Tumor bed boosts were delivered in 99% of FF-WBI patients. We evaluated dose-volume histogram parameters for target volumes and organs-at-risk and radiation-related toxicities during RT or within 6 months after the end of RT.

RESULTS

Target volume coverage was acceptable in both groups, with mean 96% of the target volumes receiving 95% of the prescribed doses and 0 cm3 within target volumes exceeding 105% of the prescribed doses. S-APBI resulted in small, but statistically significant, reductions in the radiation dose delivered to the ipsilateral breast, contralateral breast, lungs, heart, and coronary artery compared with FF-WBI. Comparing WBI to APBI, the mean contralateral breast dose, ipsilateral lung V20 Gy, mean contralateral lung dose, and mean heart dose, were reduced by 89%, 78%, 73%, and 29%, respectively. With median follow-up periods of 32 months for s-APBI and 19 months for FF-WBI, acute toxicity was assessable in all patients. The risks of any grade acute toxicity were 21% for s-APBI and 25% for FF-WBI (p = .117). Among them, grade 2 rates were 1.3% in both groups and no severe toxicity has been reported.

CONCLUSION

We found s-APBI and VMAT-based FF WBI were associated with favorable dosimetric and acute toxicity profiles. However, considering significantly less irradiated volume in the breast, lungs, and heart, APBI with advanced available technique options should be considered over any WBI-based approach for patients at low risk for local recurrence.

Association of Single Nucleotide Polymorphisms in Transforming Growth Factor-β1 Pathway and Risk of Radiation Pneumonitis in Lung Cancer Patients Treated with Thoracic Radiation Therapy

PURPOSE/OBJECTIVE(S)

Several clinical and dosimetric parameters are known to be associated with radiation-induced lung toxicity, including radiation pneumonitis (RP). Also, single nucleotide polymorphisms (SNPs) of genes in TGF-β1 pathway have a notable association with the RP. However, studies on the predictive value of SNPs for RP are still limited. Herein, we tried to develop a novel integrated predictive model for severe RP in lung cancer patients.

MATERIALS/METHODS

A total of 59 patients who were treated with definitive or preoperative radiotherapy for primary lung cancer and had DNA samples were included. Potentially functional and tagging SNPs of TGF-β1 (rs1800469, rs1800471, rs1982073, and rs11466345), BMP2 (rs235768, rs3178250, rs1979855, and rs170986), and BMP4 (rs17563, rs4898820, and rs762642) were genotyped. Logistic regression was performed to build severe (grade ≥2) RP prediction models, and best subset selection algorithm with L0 and L2 regulations was used for variable selection. Only clinical/dosimetric variables were evaluated in model 1, and those variables as well as SNPs were included in model 2. Using beta coefficient obtained by the logistic regression, a scoring system was also developed.

RESULTS

With median follow-up of 39.7 months, severe RP occurred in 20.3% of patients. In model 1, age (>66) and PTV volume (≥300 cc) were significant factors (p = 0.016, OR 8.820 [95% CI, 1.730-63.800]; and p = 0.024, OR 7.440 [95% CI, 1.460-52.100], respectively). In model 2, the above two factors (p = 0.010, OR 16.200 [95% CI, 2.440-187.000] and p = 0.025, OR 10.100 [95% CI, 1.610-105.000]) and the AG/GG genotype in BMP2 rs1979855 were significant factors (p = 0.031, OR 7.260 [95% CI, 1.380-59.100]). The AUC was significantly higher in model 2 than in model 1 (0.822 vs. 0.741, p = 0.029). According to developed scoring system, patients with a score >2.8 are more likely to experience severe RP (AUC 0.829).

CONCLUSION

BMP2 rs1979855 could serve as a reliable biomarker for predicting RP while significantly improving predictive power compared to when only clinical factors were used.

Distribution and Failure Patterns of Primary Central Nervous System Lymphoma Related to Hippocampus

PURPOSE/OBJECTIVE(S)

Hippocampus (HC) injury by conventional whole brain radiotherapy (C-WBRT) contributes to the neurocognitive decline in primary central nervous system lymphoma (PCNSL). Hippocampal avoidance (HA-WBRT) could minimize neurocognitive impairment by reducing the radiation dose to HC. However, its feasibility in PCNSL has not been examined regarding the incidence of HC involvement and failures. In this retrospective study, we assessed the risk of hippocampal area involvement at diagnosis and after treatments in PCNSL patients.

MATERIALS/METHODS

We identified 278 immunocompetent PCNSL patients diagnosed between 2000 and 2020. After high dose methotrexate-based induction chemotherapy, patients were observed or given consolidation therapy including RT, cytarabine alone, or autologous stem cell transplantation (ASCT). HC was contoured on T1 MRI image and expanded with a 5mm margin, generating hippocampal avoidance region (HAR). The extent of initial and recurrent lesions was evaluated using pre-induction and post-consolidation T1 contrast-enhanced MRI images. HC failure was defined as recurrence or progression at HAR and those who progressed after induction were excluded. The median follow up was 38.7 months (3.1-239.4).

RESULTS

Of 278 patients diagnosed with PCNSL, 39.9% of them had initial lesions at HAR (Figure 1a). After induction therapy, 212 evaluable patients received following treatments: RT (n = 145, 68.4%) consisting of C-WBRT (n = 114), HA-WBRT (n = 23), and focal RT (n = 8), observation (n = 38, 17.9%), cytarabine only (24, 11.3%), and ASCT (n = 5, 2.4%). Intracranial failures occurred in 47.6% (n = 101) of patients, with 33.7% (n = 34) of them in HAR (Figure 1b). The multivariate analysis identified multifocal disease (HR 3.86, 95% CI 1.15-9.73, p = 0.004) as the only factor associated with the risk of HC failure. Those with unifocal lesion outside HAR showed the lowest HC failure rate, 7.0%, while the highest HC failure rate, 25.4% was observed in the subgroup with multifocal disease within HAR at diagnosis (Figure 2a). In the lowest risk group (unifocal lesion outside HAR, n = 66), C-WBRT was not significantly associated with HC failure (HR 0.57, CI 0.09-3.33, P = .572, Figure 2b) or intracranial failure (HR 0.88, CI 0.40-1.91, P = .748).

CONCLUSION

Our data suggest the HA-WBRT could be explored in patients whose lesion is unifocal and located outside HAR. For patients without initial HAR involvement, hippocampal including WBRT did not significantly change HC failure. Further prospective study will be warranted to assess the feasibility of HA-WBRT in the subgroup with low risk of HC failure.

A Photograph-Based Visualization and Prediction Framework for Radiation-Induced Dermatitis

PURPOSE/OBJECTIVE(S)

This study aimed to suggest a photograph-based prediction system for acute radiation-induced dermatitis (RID), which can be applied to notify patients about the risk of the development of skin discomfort during radiotherapy.

MATERIALS/METHODS

The proposed system compared the spatial dose distribution with the RID region using the following methods. Skin photographs of patients were taken using an RGB-depth camera to acquire the shape information of RID. The skin surface data measured from the camera was registered with the shape of the external body contour using an iterative closest point algorithm. Spatial dose distribution of skin was extracted from the external body contour to a depth of 2 mm and projected onto the plane of the skin photograph using a transformation matrix for skin depth data. To compare the spatial distribution of skin dose with the shape of RID, the region of RID in patients' skin was delineated on photographs into three toxicity symptoms referring to the CTCAE criteria grade 1 (skin redness), grade 2 (dry desquamation), and grade 3 (moist desquamation). The degree of overlap between the shape of each RID and skin dose distribution was evaluated using the dice similarity coefficient (DSC). Threshold doses for predicting RID occurrence were estimated by skin isodose lines with the highest DSC. The developed system was validated using data from 19 patients who received volumetric modulated arc therapy for head-neck cancer at a single institution.

RESULTS

Threshold doses for RID grades 1, 2, and 3 were estimated using 18, 18, and 2 individual RID labels delineated on skin photographs, respectively. Isodose lines with the highest DSC for RID grades 1, 2, and 3 were calculated as 26.0 Gy, 36.5 Gy, and 54.0 Gy, respectively. A strong overlap (average DSC > 0.6) was observed between isodose skin lines and the shape of RID labels in all RID grades.

CONCLUSION

Assessing the spatial information of skin dose can be helpful in predicting acute RID. The region of RID shows a strong similarity with the skin dose distribution in head-neck patients. Visualization of skin dose on the patient photograph is potent to patient education for preparing the cosmetic discomfort during radiotherapy, which may lead to the improvement of the patient satisfaction in treatment.

Clinicogenetic characteristics and the effect of radiation on the neural stem cell niche in subventricular zone-contacting glioblastoma

BACKGROUND AND PURPOSE

Neural stem cells (NSCs) in the subventricular zone (SVZ) are recognized as the cellular origin of glioblastoma (GBM) and a potential therapeutic target. However, the characteristics of SVZ contacting GBM (SVZ + GBM) and radiotherapeutic strategies for NSCs are still controversial. Here, we investigated the clinicogenetic features of SVZ + GBM and evaluated the dose effect of NSC irradiation depending on SVZ involvement.

MATERIALS AND METHODS

We identified 125 patients with GBM treated with surgery followed by chemoradiotherapy. The genomic profiles were obtained by next-generation sequencing targeting 82 genes. NSCs in the SVZ and hippocampus were contoured using standardized methods, and dosimetric factors were analyzed. SVZ + GBM was defined as GBM with SVZ involvement in a T1 contrast-enhanced image. Progression-free survival (PFS) and overall survival (OS) were used as endpoints.

RESULTS

The number of patients with SVZ + GBM was 95 (76%). SVZ + GBM showed lower PFS than GBM without SVZ involvement (SVZ-GBM) (median 8.6 vs. 11.5 months, p = 0.034). SVZ contact was not associated with any specific genetic profile but was an independent prognostic factor in multivariate analysis. In SVZ + GBM, patients receiving high doses to the ipsilateral NSC region showed significantly better OS (HR = 1.89, p = 0.011) and PFS (HR = 1.77, p = 0.013). However, in SVZ-GBM, high doses to the ipsilateral NSC region were associated with worse OS (HR = 0.27, p = 0.013) and PFS (HR = 0.37, p = 0.035) in both univariate and multivariate analyses.

CONCLUSION

SVZ involvement in GBM was not associated with distinct genetic features. However, irradiation of NSCs was associated with better prognosis in patients with tumors contacting the SVZ.

Erratum: Agreement and Reliability between Clinically Available Software Programs in Measuring Volumes and Normative Percentiles of Segmented Brain Regions

This corrects the article on p. 959 in vol. 23, PMID: 36175000.

Minimally-Invasive and In-Vivo Hydrogel Patterning Method for In Situ Fabrication of Implantable Hydrogel Devices

Despite advances in a wide range of device applications of hydrogels, including implantable ones, a method for deploying patterned hydrogel devices into the body in a minimally-invasive manner is not available yet. However, in situ patterning of the hydrogel in vivo has an obvious advantage, by which incision surgery for implantation of the hydrogel device can be avoided. Here, a minimally-invasive and in vivo hydrogel patterning method for in situ fabrication of implantable hydrogel devices is presented. The sequential application of injectable hydrogels and enzymes, with assistance of minimally-invasive surgical instruments, enables the in vivo and in situ hydrogel patterning. This patterning method can be achieved by adopting an appropriate combination of the sacrificial mold hydrogel and the frame hydrogel, in consideration of unique material properties of the hydrogels such as high softness, facile mass transfer, biocompatibility, and diverse crosslinking mechanisms. In vivo and in situ patterning of the hydrogels functionalized with nanomaterials is also demonstrated to fabricate the wireless heater and tissue scaffold, showcasing broad applicability of the patterning method.

Dynamic contrast-enhanced MRI radiomics model predicts epidermal growth factor receptor amplification in glioblastoma, IDH-wildtype

PURPOSE

To develop and validate a dynamic contrast-enhanced (DCE) MRI-based radiomics model to predict epidermal growth factor receptor (EGFR) amplification in patients with glioblastoma, isocitrate dehydrogenase (IDH) wildtype.

METHODS

Patients with pathologically confirmed glioblastoma, IDH wildtype, from January 2015 to December 2020, with an EGFR amplification status, were included. Patients who did not undergo DCE or conventional brain MRI were excluded. Patients were categorized into training and test sets by a ratio of 7:3. DCE MRI data were used to generate volume transfer constant (Ktrans) and extracellular volume fraction (Ve) maps. Ktrans, Ve, and conventional MRI were then used to extract the radiomics features, from which the prediction models for EGFR amplification status were developed and validated.

RESULTS

A total of 190 patients (mean age, 59.9; male, 55.3%), divided into training (n = 133) and test (n = 57) sets, were enrolled. In the test set, the radiomics model using the Ktrans map exhibited the highest area under the receiver operating characteristic curve (AUROC), 0.80 (95% confidence interval [CI], 0.65-0.95). The AUROC for the Ve map-based and conventional MRI-based models were 0.74 (95% CI, 0.58-0.90) and 0.76 (95% CI, 0.61-0.91).

CONCLUSION

The DCE MRI-based radiomics model that predicts EGFR amplification in glioblastoma, IDH wildtype, was developed and validated. The MRI-based radiomics model using the Ktrans map has higher AUROC than conventional MRI.

Deep learning based on dynamic susceptibility contrast MR imaging for prediction of local progression in adult-type diffuse glioma (grade 4)

Adult-type diffuse glioma (grade 4) has infiltrating nature, and therefore local progression is likely to occur within surrounding non-enhancing T2 hyperintense areas even after gross total resection of contrast-enhancing lesions. Cerebral blood volume (CBV) obtained from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) is a parameter that is well-known to be a surrogate marker of both histologic and angiographic vascularity in tumors. We built two nnU-Net deep learning models for prediction of early local progression in adult-type diffuse glioma (grade 4), one using conventional MRI alone and one using multiparametric MRI, including conventional MRI and DSC-PWI. Local progression areas were annotated in a non-enhancing T2 hyperintense lesion on preoperative T2 FLAIR images, using the follow-up contrast-enhanced (CE) T1-weighted (T1W) images as the reference standard. The sensitivity was doubled with the addition of nCBV (80% vs. 40%, P = 0.02) while the specificity was decreased nonsignificantly (29% vs. 48%, P = 0.39), suggesting that fewer cases of early local progression would be missed with the addition of nCBV. While the diagnostic performance of CBV model is still poor and needs improving, the multiparametric deep learning model, which presumably learned from the subtle difference in vascularity between early local progression and non-progression voxels within perilesional T2 hyperintensity, may facilitate risk-adapted radiotherapy planning in adult-type diffuse glioma (grade 4) patients.

Epigenetic Alteration of H3K27me3 as a Possible Oncogenic Mechanism of Central Neurocytoma

Central neurocytoma (CN) is a low-grade neuronal tumor that mainly arises from the lateral ventricle (LV). This tumor remains poorly understood in the sense that no driver gene aberrations have been identified thus far. We investigated immunomarkers in fetal and adult brains and 45 supratentorial periventricular tumors to characterize the biomarkers, cell of origin, and tumorigenesis of CN. All CNs occurred in the LV. A minority involved the third ventricle, but none involved the fourth ventricle. As expected, next-generation sequencing performed using a brain-tumor-targeted gene panel in 7 CNs and whole exome sequencing in 5 CNs showed no driver mutations. Immunohistochemically, CNs were robustly positive for FGFR3 (100%), SSTR2 (92%), TTF-1 (Nkx2.1) (88%), GLUT-1 (84%), and L1CAM (76%), in addition to the well-known markers of CN, synaptophysin (100%) and NeuN (96%). TTF-1 was also positive in subependymal giant cell astrocytomas (100%, 5/5) and the pituicyte tumor family, including pituicytoma and spindle cell oncocytoma (100%, 5/5). Interestingly, 1 case of LV subependymoma (20%, 1/5) was positive for TTF-1, but all LV ependymomas were negative (0/5 positive). Because TTF-1-positive cells were detected in the medial ganglionic eminence around the foramen of Monro of the fetal brain and in the subventricular zone of the LV of the adult brain, CN may arise from subventricular TTF-1-positive cells undergoing neuronal differentiation. H3K27me3 loss was observed in all CNs and one case (20%) of LV subependymoma, suggesting that chromatin remodeling complexes or epigenetic alterations may be involved in the tumorigenesis of all CNs and some ST-subependymomas. Further studies are required to determine the exact tumorigenic mechanism of CN.

Functional Magnetic Resonance Imaging and Diffusion Tensor Imaging for Language Mapping in Brain Tumor Surgery: Validation With Direct Cortical Stimulation and Cortico-Cortical Evoked Potential

OBJECTIVE

Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging-derived tractography (DTI-t) contribute to the localization of language areas, but their accuracy remains controversial. This study aimed to investigate the diagnostic performance of preoperative fMRI and DTI-t obtained with a simultaneous multi-slice technique using intraoperative direct cortical stimulation (DCS) or corticocortical evoked potential (CCEP) as reference standards.

MATERIALS AND METHODS

This prospective study included 26 patients (23-74 years; male:female, 13:13) with tumors in the vicinity of Broca's area who underwent preoperative fMRI and DTI-t. A site-by-site comparison between preoperative (fMRI and DTI-t) and intraoperative language mapping (DCS or CCEP) was performed for 226 cortical sites to calculate the sensitivity and specificity of fMRI and DTI-t for mapping Broca's areas. For sites with positive signals on fMRI or DTI-t, the true-positive rate (TPR) was calculated based on the concordance and discordance between fMRI and DTI-t.

RESULTS

Among 226 cortical sites, DCS was performed in 100 sites and CCEP was performed in 166 sites. The specificities of fMRI and DTI-t ranged from 72.4% (63/87) to 96.8% (122/126), respectively. The sensitivities of fMRI (except for verb generation) and DTI-t were 69.2% (9/13) to 92.3% (12/13) with DCS as the reference standard, and 40.0% (16/40) or lower with CCEP as the reference standard. For sites with preoperative fMRI or DTI-t positivity (n = 82), the TPR was high when fMRI and DTI-t were concordant (81.2% and 100% using DCS and CCEP, respectively, as the reference standards) and low when fMRI and DTI-t were discordant (≤ 24.2%).

CONCLUSION

fMRI and DTI-t are sensitive and specific for mapping Broca's area compared with DCS and specific but insensitive compared with CCEP. A site with a positive signal on both fMRI and DTI-t represents a high probability of being an essential language area.

Non-invasive flow mapping of parasagittal meningeal lymphatics using 2D interslice flow saturation MRI

The clearance pathways of brain waste products in humans are still under debate in part due to the lack of noninvasive imaging techniques for meningeal lymphatic vessels (mLVs). In this study, we propose a new noninvasive mLVs imaging technique based on an inter-slice blood perfusion MRI called alternate ascending/descending directional navigation (ALADDIN). ALADDIN with inversion recovery (IR) at single inversion time of 2300 ms (single-TI IR-ALADDIN) clearly demonstrated parasagittal mLVs around the human superior sagittal sinus (SSS) with better detectability and specificity than the previously suggested noninvasive imaging techniques. While in many studies it has been difficult to detect mLVs and confirm their signal source noninvasively, the detection of mLVs in this study was confirmed by their posterior to anterior flow direction and their velocities and morphological features, which were consistent with those from the literature. In addition, IR-ALADDIN was compared with contrast-enhanced black blood imaging to confirm the detection of mLVs and its similarity. For the quantification of flow velocity of mLVs, IR-ALADDIN was performed at three inversion times of 2000, 2300, and 2600 ms (three-TI IR-ALADDIN) for both a flow phantom and humans. For this preliminary result, the flow velocity of the dorsal mLVs in humans ranged between 2.2 and 2.7 mm/s. Overall, (i) the single-TI IR-ALADDIN can be used as a novel non-invasive method to visualize mLVs in the whole brain with scan time of ~ 17 min and (ii) the multi-TI IR-ALADDIN can be used as a way to quantify the flow velocity of mLVs with a scan time of ~ 10 min (or shorter) in a limited coverage. Accordingly, the suggested approach can be applied to noninvasively studying meningeal lymphatic flows in general and also understanding the clearance pathways of waste production through mLVs in humans, which warrants further investigation.

Geometric Tuning of Single-Atom FeN4 Sites via Edge-Generation Enhances Multi-Enzymatic Properties

Single-atom nanozymes (SAzymes) are considered promising alternatives to natural enzymes. The catalytic performance of SAzymes featuring homogeneous, well-defined active structures can be enhanced through elucidating structure-activity relationship and tailoring physicochemical properties. However, manipulating enzymatic properties through structural variation is an underdeveloped approach. Herein, the synthesis of edge-rich Fe single-atom nanozymes (FeNC-edge) via an H₂ O₂ -mediated edge generation is reported. By controlling the number of edge sites, the peroxidase (POD)- and oxidase (OXD)-like performance is significantly enhanced. The activity enhancement results from the presence of abundant edges, which provide new anchoring sites to mononuclear Fe. Experimental results combined with density functional theory (DFT) calculations reveal that FeN₄ moieties in the edge sites display high electron density of Fe atoms and open N atoms. Finally, it is demonstrated that FeNC-edge nanozyme effectively inhibits tumor growth both in vitro and in vivo, suggesting that edge-tailoring is an efficient strategy for developing artificial enzymes as novel catalytic therapeutics.

Genomic profiles of IDH-mutant gliomas: MYCN-amplified IDH-mutant astrocytoma had the worst prognosis

This study aimed to find any ambiguous genetic outlier for "oligodendroglioma, IDH-mutant and 1p/19q-codeleted (O_IDH_mut)" and "astrocytoma, IDH-mutant (A_IDH_mut)" and to redefine the genetic landscape and prognostic factors of IDH-mutant gliomas. Next-generation sequencing (NGS) using a brain tumor-targeted gene panel, methylation profiles, and clinicopathological features were analyzed for O_IDH_mut (n = 74) in 70 patients and for A_IDH_mut (n = 95) in 90 patients. 97.3% of O_IDH_mut and 98.9% of A_IDH_mut displayed a classic genomic landscape. Combined CIC (75.7%) and/or FUBP1 (45.9%) mutations were detected in 93.2% and MGMTp methylation in 95.9% of O_IDH_mut patients. In A_IDH_mut, TP53 mutations were found in 86.3% and combined ATRX (82.1%) and TERTp (6.3%) mutations in 88.4%. Although there were 3 confusing cases, NOS (not otherwise specified) category, based on genetic profiles, but they were clearly classified by combining histopathology and DKFZ methylation classifier algorithms. The patients with MYCN amplification and/or CDKN2A/2B homozygous deletion in the A_IDH_mut category had a worse prognosis than those without these gene alterations and MYCN-amplified A_IDH_mut showed the worst prognosis. However, there was no prognostic genetic marker in O_IDH_mut. In histopathologically or genetically ambiguous cases, methylation profiles can be used as an objective tool to avoid a diagnosis of NOS or NEC (not elsewhere classified), as well as for tumor classification. The authors have not encountered a case of true mixed oligoastrocytoma using an integrated diagnosis of histopathological, genetic and methylation profiles. MYCN amplification, in addition to CDKN2A/2B homozygous deletion, should be included in the genetic criteria for CNS WHO grade 4 A_IDH_mut.

Deep learning based automatic detection algorithm for acute intracranial haemorrhage: a pivotal randomized clinical trial

Acute intracranial haemorrhage (AIH) is a potentially life-threatening emergency that requires prompt and accurate assessment and management. This study aims to develop and validate an artificial intelligence (AI) algorithm for diagnosing AIH using brain-computed tomography (CT) images. A retrospective, multi-reader, pivotal, crossover, randomised study was performed to validate the performance of an AI algorithm was trained using 104,666 slices from 3010 patients. Brain CT images (12,663 slices from 296 patients) were evaluated by nine reviewers belonging to one of the three subgroups (non-radiologist physicians, n = 3; board-certified radiologists, n = 3; and neuroradiologists, n = 3) with and without the aid of our AI algorithm. Sensitivity, specificity, and accuracy were compared between AI-unassisted and AI-assisted interpretations using the chi-square test. Brain CT interpretation with AI assistance results in significantly higher diagnostic accuracy than that without AI assistance (0.9703 vs. 0.9471, p < 0.0001, patient-wise). Among the three subgroups of reviewers, non-radiologist physicians demonstrate the greatest improvement in diagnostic accuracy for brain CT interpretation with AI assistance compared to that without AI assistance. For board-certified radiologists, the diagnostic accuracy for brain CT interpretation is significantly higher with AI assistance than without AI assistance. For neuroradiologists, although brain CT interpretation with AI assistance results in a trend for higher diagnostic accuracy compared to that without AI assistance, the difference does not reach statistical significance. For the detection of AIH, brain CT interpretation with AI assistance results in better diagnostic performance than that without AI assistance, with the most significant improvement observed for non-radiologist physicians.

Penetrative and Sustained Drug Delivery Using Injectable Hydrogel Nanocomposites for Postsurgical Brain Tumor Treatment

Postsurgical treatment of glioblastoma multiforme (GBM) by systemic chemotherapy and radiotherapy is often inefficient. Tumor cells infiltrating deeply into the brain parenchyma are significant obstacles to the eradication of GBM. Here, we present a potential solution to this challenge by introducing an injectable thermoresponsive hydrogel nanocomposite. As a liquid solution that contains drug-loaded micelles and water-dispersible ferrimagnetic iron oxide nanocubes (wFIONs), the hydrogel nanocomposite is injected into the resected tumor site after surgery. It promptly gelates at body temperature to serve as a soft, deep intracortical drug reservoir. The drug-loaded micelles target residual GBM cells and deliver drugs with a minimum premature release. Alternating magnetic fields accelerate diffusion through heat generation from wFIONs, enabling penetrative drug delivery. Significantly suppressed tumor growth and improved survival rates are demonstrated in an orthotopic mouse GBM model. Our system proves the potential of the hydrogel nanocomposite platform for postsurgical GBM treatment.

Quantifying infarct core volume in ischemic stroke: What is the optimal threshold and parameters of computed tomography perfusion?

OBJECTIVE

Although computed tomography perfusion (CTP) is used to select and guide decision-making processes in patients with acute ischemic stroke, there is no clear standardization of the optimal threshold to predict ischemic core volume accurately. The infarct core volume with a relative cerebral blood flow(rCBF) threshold of < 30% is commonly used. We aimed to assess the volumetric agreement of the infarct core volume with different CTP parameters and thresholds using CTP software (RAPID, VITREA) and the infarct volume on diffusion-weighted imaging (DWI), with a short interval time (within 60 min) between CTP and follow-up DWI.

MATERIALS AND METHODS

This retrospective study included 42 acute ischemic stroke patients with occlusion of the large artery in the anterior circulation between April 2017-November 2020. RAPID identified infarct core as tissue rCBF < 20-38%. VITREA defined the infarct core as cerebral blood volume (CBV) < 26-56%. Olea Sphere was used to measure infarct core volume on DWI. The CTP-infarct core volume with different thresholds of perfusion parameters (CBF threshold vs CBV threshold) were compared with DWI-infarct core volumes.

RESULTS

The median time between CTP and DWI was 37.5min. The commonly used threshold of CBV< 41% (4.3 mL) resulted in lower median infarct core volume difference compared to the commonly used thresholds of rCBF < 30% (8.2mL). On the other hand, the optimal thresholds of CBV < 26% (-1.0mL; 95% CI, -53.9 to 58.1 mL; 0.945) resulted in the lowest median infarct core volume difference, narrowest limits of agreement, and largest interclass correlation coefficient compared with the optimal thresholds of rCBF < 38% (4.9 mL; 95% CI, -36.4 to 62.9 mL; 0.939).

CONCLUSION

Our study found that the both optimal and commonly used thresholds of CBV provided a more accurate prediction of the infarct core volume in patients with AIS than rCBF.

Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning

BACKGROUND

Accurate and rapid measurement of the MRI volume of meningiomas is essential in clinical practice to determine the growth rate of the tumor. Imperfect automation and disappointing performance for small meningiomas of previous automated volumetric tools limit their use in routine clinical practice.

PURPOSE

To develop and validate a computational model for fully automated meningioma segmentation and volume measurement on contrast-enhanced MRI scans using deep learning.

STUDY TYPE

Retrospective.

POPULATION

A total of 659 intracranial meningioma patients (median age, 59.0 years; interquartile range: 53.0-66.0 years) including 554 women and 105 men.

FIELD STRENGTH/SEQUENCE

The 1.0 T, 1.5 T, and 3.0 T; three-dimensional, T₁ -weighted gradient-echo imaging with contrast enhancement.

ASSESSMENT

The tumors were manually segmented by two neurosurgeons, H.K. and C.-K.P., with 10 and 26 years of clinical experience, respectively, for use as the ground truth. Deep learning models based on U-Net and nnU-Net were trained using 459 subjects and tested for 100 patients from a single institution (internal validation set [IVS]) and 100 patients from other 24 institutions (external validation set [EVS]), respectively. The performance of each model was evaluated with the Sørensen-Dice similarity coefficient (DSC) compared with the ground truth.

STATISTICAL TESTS

According to the normality of the data distribution verified by the Shapiro-Wilk test, variables with three or more categories were compared by the Kruskal-Wallis test with Dunn's post hoc analysis.

RESULTS

A two-dimensional (2D) nnU-Net showed the highest median DSCs of 0.922 and 0.893 for the IVS and EVS, respectively. The nnU-Nets achieved superior performance in meningioma segmentation than the U-Nets. The DSCs of the 2D nnU-Net for small meningiomas less than 1 cm³ were 0.769 and 0.780 with the IVS and EVS, respectively.

DATA CONCLUSION

A fully automated and accurate volumetric measurement tool for meningioma with clinically applicable performance for small meningioma using nnU-Net was developed.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

MYB/MYBL1::QKI fusion-positive diffuse glioma

The MYB/MYBL1::QKI fusion induces the protooncogene, MYB, and deletes the tumor suppressor gene, QKI. MYB/MYBL1::QKI rearrangement was previously reported only in angiocentric glioma (AG) and diffuse low-grade glioma. This report compares 2 tumors containing the MYB/MYBL1::QKI fusion: a diffuse pediatric-type high-grade glioma (DPedHGG) in an 11-year-old boy and an AG in a 46-year-old woman. We used immunohistochemistry, next-generation sequencing, and methylation profiling to characterize each tumor and compare our findings to the literature on AG and tumors with the MYB/MYBL1::QKI rearrangement. Both tumors were astrocytic with angiocentric patterns. The MYB::QKI fusion-positive DPedHGG, which recurred once, was accompanied by TP53 mutation and amplification of CDK6 and KRAS, suggesting malignant transformation secondary to additional genetic aberrations. The second case was the adult AG with MYBL1::QKI fusion, which mimicked ependymoma based on histopathology and its dot- and ring-like epithelial membrane antigen positivity. Combined with a literature review, our results suggest that MYB/MYBL1 alterations are not limited to low-grade gliomas, including AG. AG is most common in the cerebra of children and adolescents but exceptional cases occur in adults and the acquisition of additional genetic mutations may contribute to high-grade glioma. These cases further demonstrate that molecular characteristics, morphologic features, and clinical context are essential for diagnosis.

Ex vivo NMR metabolomics approach using cerebrospinal fluid for the diagnosis of primary CNS lymphoma: Correlation with MR imaging characteristics

PURPOSE

Primary central nervous system lymphoma (PCNSL) is an uncommon extranodal non-Hodgkin's lymphoma. Here, the feasibility of nuclear magnetic resonance (NMR) metabolomics for the diagnosis and prognosis prediction of PCNSL, as well as its correlation with magnetic resonance imaging (MRI) characteristics, was assessed.

PATIENTS AND METHODS

Cerebrospinal fluid (CSF) samples from PCNSL and normal groups (n = 41 for each) were obtained along with MRI data including pre- and postcontrast as well as T1-, T2-, and diffusion-weighted imaging for the treatment-naïve PCNSL patients (n = 24). The CSF samples were analyzed using nuclear magnetic resonance (NMR).

RESULTS

The CSF NMR metabolomic exhibited clear differences with a diagnostic sensitivity of 100% and a specificity of 97.6%. The citrate level of the leptomeningeal enhancement (LE) (+) group was significantly lower than that of the LE (-) group (p = 0.018). In addition, the MRI apparent diffusion coefficient (ADC) value of the tumor was positively correlated with the glucose level (p = 0.025). However, none of the marker metabolites were significant prognosis predictors in univariate analysis.

CONCLUSIONS

In conclusion, the NMR metabolomics could be helpful to diagnose PCNSL, but not for the prognosis, and MRI features (LE or ADC) can reflect the metabolic profiles of PCNSL.

Assessment of Esophageal Reconstruction via Bioreactor Cultivation of a Synthetic Scaffold in a Canine Model

Objectives

The use of tissue-engineered materials for esophageal reconstruction is a technically challenging task in animal and requires bioreactor training for enhancing cellular reactivity. Recently, there have been many attempts at esophageal tissue engineering, but the success rate has been limited due to difficulty in initial epithelialization in the special environment of peristalsis. The purpose of this study was to evaluate the potential of an artificial esophagus that can enhance the regeneration of esophageal mucosa and muscle through the optimal combination of a double-layered polymeric scaffold and a custom-designed MSC-based bioreactor system in canine model.

Methods

We fabricated a novel double-layered scaffold as a tissue-engineered esophagus using an electrospinning technique. Prior to transplantation, human-derived mesenchymal stem cells were seeded into the lumen of the scaffold, and bioreactor cultivation was performed to enhance cellular reactivity. After 3 days of cultivation using a bioreactor system, tissue-engineered artificial esophagus was transplanted into a partial esophageal defect (5 x 3 cm-long resection) in a canine model.

Results

From the SEM analysis, electrospun fibers in a tubular scaffold were found to be randomly and circumferentially oriented toword the inner and outer surfaces, respectively. Complete recovery of the esophageal mucosa was confirmed by endoscopic analysis and SEM. Esophagogastroduodenoscopy (EGD) and CT analysis also showed that there were no signs of leakage or stricture and that there were normal lumen with complete epithelialization. Significant regeneration of the mucosal layer was observed by keratin 5 immunostaining. From the α-SMA immunostaining, esophageal muscle regeneration was significantly increased in the graft (12 months) group compared with the graft (6 months) group.

Conclusion

A custom-designed bioreactor cultured electrospun PU scaffolds can be a promising approach for esophageal tissue engineering.

A New Approach to Quantify and Grade Radiation Dermatitis Using Deep-Learning Segmentation in Skin Photographs

AIMS

Objective evaluation of radiation dermatitis is important for analysing the correlation between the severity of radiation dermatitis and dose distribution in clinical practice and for reliable reporting in clinical trials. We developed a novel radiation dermatitis segmentation system based on convolutional neural networks (CNNs) to consistently evaluate radiation dermatitis.

MATERIALS AND METHODS

The radiation dermatitis segmentation system is designed to segment the radiation dermatitis occurrence area using skin photographs and skin-dose distribution. A CNN architecture with a dilated convolution layer and skip connection was designed to estimate the radiation dermatitis area. Seventy-three skin photographs obtained from patients undergoing radiotherapy were collected for training and testing. The ground truth of radiation dermatitis segmentation is manually delineated from the skin photograph by an experienced radiation oncologist and medical physicist. We converted the skin photographs to RGB (red-green-blue) and CIELAB (lightness (L^∗), red-green (a^∗) and blue-yellow (b^∗)) colour information and trained the network to segment faint and severe radiation dermatitis using three different input combinations: RGB, RGB + CIELAB (RGBLAB) and RGB + CIELAB + skin-dose distribution (RGBLAB_D). The proposed system was evaluated using the Dice similarity coefficient (DSC), sensitivity, specificity and normalised Matthews correlation coefficient (nMCC). A paired t-test was used to compare the results of different segmentation performances.

RESULTS

Optimal data composition was observed in the network trained for radiation dermatitis segmentation using skin photographs and skin-dose distribution. The average DSC, sensitivity, specificity and nMCC values of RGBLAB_D were 0.62, 0.61, 0.91 and 0.77, respectively, in faint radiation dermatitis, and 0.69, 0.78, 0.96 and 0.83, respectively, in severe radiation dermatitis.

CONCLUSION

Our study showed that CNN-based radiation dermatitis segmentation in skin photographs of patients undergoing radiotherapy can describe radiation dermatitis severity and pattern. Our study could aid in objectifying the radiation dermatitis grading and analysing the reliable correlation between dosimetric factors and the morphology of radiation dermatitis.

Differential Background Music as Attentional Resources Interacting with Cognitive Control

We examined the effects of background music on cognitive task performances using different musical arrangements from an excerpt of Mozart's Piano Sonata K.448. The participants were 126 university students: 70 music majors and 56 nonmusic majors. Three types of musical arrangements were used as background conditions: rhythm-only, melody, and original music conditions. Participants were asked to perform cognitive tasks in the presence of each music condition. The participants' percentage of completed items and accuracy on these tasks were compared for music and nonmusic majors, controlling for the effect of perceived level of arousal and their performance during no background music. Whether a participant's perceptions of background music predicted their cognitive performance was also analyzed. We found that music majors demonstrated decreased task performance for the original background condition, while nonmusic majors demonstrated no significant differences in performance across the arrangements. When pitch or rhythm information was modified, emotional valence and arousal were perceived differently. Perception of the complexity of the background music depending on the arrangement type differed between music majors and nonmusic majors. While the perceived complexity significantly predicted nonmusic majors' cognitive performance, its predictive effect was not found in music majors. The findings imply that perceptions of musical arrangements in terms of expectancy and complexity can be critical factors in determining how arrangements affect concurrent cognitive activity, while suggesting that music itself is not a facilitating or detrimental factor for cognitive performance.

BIOM-18. CLINICOGENOMIC PREDICTORS FOR THE PATTERN OF FAILURE IN HIGH-GRADE GLIOMA

Background

High-grade gliomas, the most common primary brain tumors, are highly lethal and treatment options remain limited. Despite advances in genomic technologies, there are few molecular biomarkers to guide precision medicine for high-grade glioma. Here, we aimed to identify the clinicogenomic features associated with its prognosis and recurrence patterns.

MATERIALS/METHODS

Our single-institution retrospective analysis included 182 patients diagnosed with high-grade gliomas who underwent next-generation sequencing targeting 82 brain tumor-relevant genes. Clinicopathological status, treatment characteristics, survival, and genomic profiles were analyzed.

RESULTS

At a median follow-up of 23 months (range, 2-229 months), 151 patients (83%) had progression or recurrences. Local and distant recurrences were observed in 132 (72.5%) and 101 (54.9%) patients, respectively. The most common genomic variants in high-grade gliomas were TP53 (42.9%), IDH1/2 (23.1%), TERT promoter (38.5%), ATRX (13.2%), H3F3A (7.1%), and SETD2 (6.0%) mutation. Regarding copy number variants, amplification of EGFR (20.9%), PDGFRA (9.9%), MYCN (2.2%) and loss of CDKN2A/2B (49.5%), PTEN (37.9%), RB1 (17.6%), and 1p19q codeletion(9.3%) were the most common copy number aberrations. On multivariate cox regression anlalysis, MYCN amplification (HR 6.08 95% CI 1.91-19.35, p = 0.002), and SETD2 mutation (HR 0.19 95% CI 0.06-0.62, p =0.06) were independent predictors of overall survival, in relation to previously established prognostic factors including age, Eastern Cooperative Oncology Group Performance Status (ECOG PS) scale, extent of resection, MGMT promoter methylation, and IDH1/2 mutation. Interestingly, MYCN amplification (HR 5.24 95% CI 1.69-16.27, p = 0.004), SETD2 mutation (HR 0.35 95% CI 0.12-0.99, p =0.048) were independent predictors of failure from distant recurrences.

CONCLUSION

The assessment of genomic characteristics in conjunction with pattern of failure for high-grade glioma aids to identify patients who are likely to benefit from personalized medicine. We identified SETD2 mutation, and MYCN as a prognostic biomarkers with potential therapeutic implications in patients with high-grade glioma.

Glymphatic MRI techniques in sleep and neurodegenerative diseases

PURPOSE OF REVIEW

The purpose of this review article is to summarize the current in-vivo imaging techniques for the evaluation of the glymphatic function and discuss the factors influencing the glymphatic function and research directions in the future.

RECENT FINDINGS

The glymphatic system allows the clearance of metabolic waste from the central nervous system (CNS). The glymphatic pathway has been investigated using intrathecal or intravenous injection of a gadolinium-based contrast agent (GBCA) on MRI, so-called glymphatic MRI. The glymphatic MRI indirectly visualizes the dynamic CSF flow and evaluated the glymphatic function in the animal and human models. Several clinical and preclinical studies using glymphatic MRI have confirmed that the glymphatic function is impaired in neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, and idiopathic normal pressure hydrocephalus. Furthermore, physiologic process such as sleep facilitates the glymphatic clearance, thus clearing accumulation of protein deposition, such as amyloid or tau, potentially delaying the progression of neurodegenerative diseases.

SUMMARY

The glymphatic system plays a crucial role in clearing metabolic wastes in the brain. Glymphatic MR imaging using GBCA administration serves as a functional imaging tool to measure the glymphatic function and investigate various pathophysiologies of neurodegenerative diseases.

Cluster Analysis of DSC MRI, Dynamic Contrast-Enhanced MRI, and DWI Parameters Associated with Prognosis in Patients with Glioblastoma after Removal of the Contrast-Enhancing Component: A Preliminary Study

BACKGROUND AND PURPOSE

No report has been published on the use of DSC MR imaging, DCE MR imaging, and DWI parameters in combination to create a prognostic prediction model in glioblastoma patients. The aim of this study was to develop a machine learning-based model to find preoperative multiparametric MR imaging parameters associated with prognosis in patients with glioblastoma. Normalized CBV, volume transfer constant, and ADC of the nonenhancing T2 high-signal-intensity lesions were evaluated using K-means clustering.

MATERIALS AND METHODS

A total of 142 patients with glioblastoma who underwent preoperative MR imaging and total resection were included in this retrospective study. From the normalized CBV, volume transfer constant, and ADC maps, the parametric data were sorted using the K-means clustering method. Patients were divided into training and test sets (ratio, 1:1), and the optimal number of clusters was determined using receiver operating characteristic analysis. Kaplan-Meier survival analysis and log-rank tests were performed to identify potential parametric predictors. A multivariate Cox proportional hazard model was conducted to adjust for clinical predictors.

RESULTS

The nonenhancing T2 high-signal-intensity lesions were divided into 6 clusters. The cluster (class 4) with the relatively low normalized CBV and volume transfer constant value and the lowest ADC values was most associated with predicting glioblastoma prognosis. The optimal cutoff of the class 4 volume fraction of nonenhancing T2 high-signal-intensity lesions predicting 1-year progression-free survival was 9.70%, below which the cutoff was associated with longer progression-free survival. Two Kaplan-Meier curves based on the cutoff value showed a statistically significant difference (P = .037). When we adjusted for all clinical predictors, the cluster with the relatively low normalized CBV and volume transfer constant values and the lowest ADC value was an independent prognostic marker (hazard ratio, 3.04; P = .048). The multivariate Cox proportional hazard model showed a concordance index of 0.699 for progression-free survival.

CONCLUSIONS

Our model showed that nonenhancing T2 high-signal-intensity lesions with the relatively low normalized CBV, low volume transfer constant values, and the lowest ADC values could serve as useful prognostic imaging markers for predicting survival outcomes in patients with glioblastoma.

Comparison of trans-radial access and femoral access in cardiogenic shock patient who had undergone primary percutaneous coronary intervention from SMART RESCUE trial

Introduction

Throughout the years of percutaneous coronary intervention (PCI), the debate regarding access route, whether it being trasns-radial or femoral, is an ongoing agenda yet to be solved. Recent guidelines suggest trans-radial approach as an option to be considered in acute coronary syndrome (ACS). However, data on cardiogenic shock patients undergoing PCI is relatively sparse.

Purpose

Compare the clinical implication of trans-radial and femoral approach in cardiogenic shock patients who had undergone PCI.

Method

Cardiogenic shock patients who had undergone PCI from January 2014 to December of 2018 were enrolled. Patients were divided according to their access route respectfully. Primary outcome was composite endpoints including all-cause death, re-admission due to heart failure, myocardial infarction (MI) and cerebrovascular accident.

Result

A total of 694 (572 via femoral approach, 122 via radial approach) cardiogenic shock patients who received PCI were enrolled. Mean age femoral and radial groups was 66.59±12.51 and 66.66±12.8 respectfully. Disease severity was higher for femoral patients compared to radial patients as represented by their LM involvement, mechanical organ support (extracorporeal membrane oxygenation, continuous renal replacement therapy, mechanical ventilation), left ventricular ejection fraction (LVEF) and vasoactive inotropic score. Cox regression analysis after adjusting for conventional risk factors showed that femoral route was a poor prognosticator with respect to composite endpoints (HR=2.059, 95% CI 1.249–3.397, p value = 0.005). Radial approach patients had higher survival probability compared to femoral approach patients (Figure 1).

Conclusion

Radial approach in cardiogenic shock patients who are in need for PCI with relatively less severe clinical condition could be a reasonable option for access route.

Funding Acknowledgement

Type of funding sources: Private hospital(s). Main funding source(s): Inha University Hospital

Prognostic impact of plasma glucose on cardiogenic shock patients with or without diabetes ellitus: smart rescue trial

Background

Even though the presence of hyperglycemia has shown to affect the clinical outcome of cardiogenic shock patients, the extent of hyperglycemia and its association with prognosis have not been fully addressed in large population

Purpose

Investigate the clinical relationship between hyperglycemic status and in-hospital mortality in cardiogenic shock patients

Method

A total of 1,177 consecutive cardiogenic shock patients were enrolled from January 2014 to December of 2018 at 12 hospitals in South Korea. The primary outcome was in-hospital mortality. Patients were divided into four groups according to their initial plasma glucose level in each of diabetes patients (n=752) and non-diabetes patients (n=425); group 1 (≤8 mmol/L), group 2 (8–12 mmol/L), group 3 (12–16 mmol/L) and group 4 (≥16 mmol/L).

Results

The groups with higher admission plasma glucose were associated with lower systolic blood pressure and higher lactic acid level in both diabetic and non-diabetic patients. In-hospital mortality increased in groups with higher admission plasma glucose level in non-diabetic patients (group-1:24.2%, group-2: 28.6%, group-3: 38.1%, group-4: 49.0%, p&lt;0.01) whereas in diabetic patients, mortality and admission plasma glucose level showed no significant association (group-1: 45%, group-2: 35.4%, group-3: 33.3%, group-4: 43.1%, p=0.26). Even after Multivariate analysis, high plasma glucose was an independent predictor of in-hospital mortality in non-diabetic patients

Conclusion

In cardiogenic shock patients, plasma glucose obtained at admission was associated with in-hospital mortality in non-diabetic patients

Funding Acknowledgement

Type of funding sources: Private hospital(s). Main funding source(s): Inha University hospital

Impact of obesity paradox between genders on in-hospital mortality in cardiogenic shock: a retrospective cohort study

Background

In a few studies, obesity was associated with better outcomes in patients with cardiogenic shock (CS). Although this phenomenon, the “obesity paradox”, reportedly manifests differently based on sex in other disease entities, it has not yet been investigated in CS patients.

Methods and results

1,227 patients with CS from The REtrospective and prospective observational Study to investigate Clinical oUtcomes and Efficacy of left ventricular assist device for Korean patients with cardiogenic shock (RESCUE) registry in Korea were analyzed. The study population was classified into obese and non-obese groups according to Asian-Pacific criteria (BMI &gt;25.0 kg/m2 for obese). Clinical impact of obesity on in-hospital mortality according to sex was analyzed using logistic regression analysis and restricted cubic spline curves. In-hospital mortality rate was significantly lower in obese men than non-obese men (34.2% vs. 24.1%, p=0.004) while the difference was not significant in women (37.3% vs. 35.8%, p=0.884). As a continuous variable, higher BMI showed a protective effect in men conversely, BMI was not associated with clinical outcomes in women. Comparing to normal-weight patients, obesity was associated with a decreased risk of in-hospital death in men (multivariable-adjusted OR 0.63, CI 0.43–0.92, p=0.016), not in women (multivariable-adjusted OR 0.94, 95% CI 0.55–1.61, p=0.828). Interaction P value for the association between BMI and sex was 0.023.

Conclusions

Obesity paradox exists and apparently occurs in men among CS patients. The differential effect of BMI on in-hospital mortality was observed according to sex.

Funding Acknowledgement

Type of funding sources: None.

Risk factors and clinical effects of late leaflet thrombosis after transcatheter aortic valve replacement

Background

As the indications for trans-catheter aortic valve replacement (TAVR) expand, it is expected that the number of TAVR patients would increase and the follow-up duration would be longer. It is known that the incidence of leaflet thrombosis is higher in TAVR than in surgical aortic valve replacement (SAVR), but not much is known about the risk factors of late leaflet thrombosis in TAVR.

Aim

Therefore, in this study, the incidence and risk factors of late leaflet thrombosis at late term after TAVR and the effect on clinical course of late leaflet thrombosis would be investigated.

Method

There were 176 patients undergone TAVR from January 2015 to October 2020 in one tertiary hospital of south korea. 94 patients had follow-up cardiovascular computed tomography (CT) between 3 months and 2 years after TAVR. Among 94 patients, late leaflet thrombosis was discovered at 20 patients, and risk factors were analyzed by comparing clinical factors, echocardiographic and cardiovascular CT information, and angiographic data between the group with and without late leaflet thrombosis. And the difference in aortic valve hemodynamics between the group with and without leaflet thrombosis was examined and clinical outcomes were compared. Clinical outcome was defined as the composite of all-cause death, stroke, heart failure (HF) admission, redo-aortic valve (AV) replacement and major bleeding after detection of late leaflet thrombosis.

Results

Indexed mean sinus of Valsalva diameter, AV calcium score and post procedure estimated orifice area (EOA) had predictability of late leaflet thrombosis with AUC value of 0.670 (95% CI [0.546–0.795], p value = 0.020), AUC value of 0.698 (95% CI [0.544–0.851], p value = 0.012) and AUC value of 0.665 (95 percent CI [0.548–0.782], p value = 0.031), respectively (Figure 1).

In echocardiography performed at the time of follow-up CT, AV max velocity and AV mean pressure gradient were higher in thrombosis group and EOA and Doppler velocity index were lower in thrombosis group than in no thrombosis group within normal range (Figure 2). Clinical outcome was not significant different between the two groups (log rank p value = 0.560).

Conclusion

Larger indexed sinus of Valsalva diameter, higher AV calcium score and smaller post procedure AV EOA were risk factors for late leaflet thrombosis after TAVR. Subclinical late leaflet thrombosis have a benign course when properly managed.

Funding Acknowledgement

Type of funding sources: None.

Agreement and Reliability between Clinically Available Software Programs in Measuring Volumes and Normative Percentiles of Segmented Brain Regions

OBJECTIVE

To investigate the agreement and reliability of estimating the volumes and normative percentiles (N%) of segmented brain regions among NeuroQuant (NQ), DeepBrain (DB), and FreeSurfer (FS) software programs, focusing on the comparison between NQ and DB.

MATERIALS AND METHODS

Three-dimensional T1-weighted images of 145 participants (48 healthy participants, 50 patients with mild cognitive impairment, and 47 patients with Alzheimer's disease) from a single medical center (SMC) dataset and 130 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset were included in this retrospective study. All images were analyzed with DB, NQ, and FS software to obtain volume estimates and N% of various segmented brain regions. We used Bland-Altman analysis, repeated measures ANOVA, reproducibility coefficient, effect size, and intraclass correlation coefficient (ICC) to evaluate inter-method agreement and reliability.

RESULTS

Among the three software programs, the Bland-Altman plot showed a substantial bias, the ICC showed a broad range of reliability (0.004-0.97), and repeated-measures ANOVA revealed significant mean volume differences in all brain regions. Similarly, the volume differences of the three software programs had large effect sizes in most regions (0.73-5.51). The effect size was largest in the pallidum in both datasets and smallest in the thalamus and cerebral white matter in the SMC and ADNI datasets, respectively. N% of NQ and DB showed an unacceptably broad Bland-Altman limit of agreement in all brain regions and a very wide range of ICC values (-0.142-0.844) in most brain regions.

CONCLUSION

NQ and DB showed significant differences in the measured volume and N%, with limited agreement and reliability for most brain regions. Therefore, users should be aware of the lack of interchangeability between these software programs when they are applied in clinical practice.

An exploratory study on functional connectivity after mild traumatic brain injury: Preserved global but altered local organization

INTRODUCTION

This study aimed to investigate alterations in whole-brain functional connectivity after a concussion using graph-theory analysis from global and local perspectives and explore the association between changes in the functional network properties and cognitive performance.

METHODS

Individuals with mild traumatic brain injury (mTBI, n = 29) within a month after injury, and age- and sex-matched healthy controls (n = 29) were included. Graph-theory measures on functional connectivity assessed using resting state functional magnetic resonance imaging data were acquired from each participant. These included betweenness centrality, strength, clustering coefficient, local efficiency, and global efficiency. Multi-domain cognitive functions were correlated with the graph-theory measures.

RESULTS

In comparison to the controls, the mTBI group showed preserved network characteristics at a global level. However, in the local network, we observed decreased betweenness centrality, clustering coefficient, and local efficiency in several brain areas, including the fronto-parietal attention network. Network strength at the local level showed mixed-results in different areas. The betweenness centrality of the right parahippocampus showed a significant positive correlation with the cognitive scores of the verbal learning test only in the mTBI group.

CONCLUSION

The intrinsic functional connectivity after mTBI is preserved globally, but is suboptimally organized locally in several areas. This possibly reflects the neurophysiological sequelae of a concussion. The present results may imply that the network property could be used as a potential indicator for clinical outcomes after mTBI.

Pathological Classification of the Intramedullary Spinal Cord Tumors According to 2021 World Health Organization Classification of Central Nervous System Tumors, a Single-Institute Experience

According to the new 2021 World Health Organization (WHO) classification of tumors of the central nervous system (CNS) the classification of the primary intramedullary spinal cord tumors (IM-SCT) follows that of CNS tumors. However, since the genetics and methylation profile of ependymal tumors depend on the location of the tumor, the 'spinal (SP)' should be added for the ependymoma (EPN) and subependymoma (SubEPN). For an evidence-based review, the authors reviewed SCTs in the archives of the Seoul National University Hospital over the past decade. The frequent pathologies of primary IM-SCT were SP-EPN (45.1%), hemangioblastoma (20.0%), astrocytic tumors (17.4%, including pilocytic astrocytoma [4.6%] and diffuse midline glioma, H3 K27-altered [4.0%]), myxopapillary EPN (11.0%), and SP-subEPN (3.0%) in decreasing order. IDH-mutant astrocytomas, oligodendrogliomas, glioneuronal tumors, embryonal tumors, and germ cell tumors can occur but are extremely rare in the spinal cord. Genetic studies should support for the primary IM-SCT classification. In the 2021 WHO classifications, extramedullary SCT did not change significantly but contained several new genetically defined types of mesenchymal tumors. This article focused on primary IM-SCT for tumor frequency, age, sex difference, pathological features, and genetic abnormalities, based on a single-institute experience.

The telomere maintenance mechanism spectrum and its dynamics in gliomas

Background

The activation of the telomere maintenance mechanism (TMM) is one of the critical drivers of cancer cell immortality. In gliomas, TERT expression and TERT promoter mutation are considered to reliably indicate telomerase activation, while ATRX mutation and/or loss indicates an alternative lengthening of telomeres (ALT). However, these relationships have not been extensively validated in tumor tissues.

Methods

Telomerase repeated amplification protocol (TRAP) and C-circle assays were used to profile and characterize the TMM cross-sectionally (n = 412) and temporally (n = 133) across glioma samples. WES, RNA-seq, and NanoString analyses were performed to identify and validate the genetic characteristics of the TMM groups.

Results

We show through the direct measurement of telomerase activity and ALT in a large set of glioma samples that the TMM in glioma cannot be defined solely by the combination of telomerase activity and ALT, regardless of TERT expression, TERT promoter mutation, and ATRX loss. Moreover, we observed that a considerable proportion of gliomas lacked both telomerase activity and ALT. This telomerase activation-negative and ALT negative group exhibited evidence of slow growth potential. By analyzing a set of longitudinal samples from a separate cohort of glioma patients, we discovered that the TMM is not fixed and can change with glioma progression.

Conclusions

This study suggests that the TMM is dynamic and reflects the plasticity and oncogenicity of tumor cells. Direct measurement of telomerase enzyme activity and evidence of ALT should be considered when defining TMM. An accurate understanding of the TMM in glioma is expected to provide important information for establishing cancer management strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01095-x.

Local Drug Delivery Strategies for Glioblastoma Treatment

Glioblastoma multiforme (GBM) is a brain tumor notorious for its malignancy. The key reason for the limited efficacy of standard treatment is the high recurrence rate of GBM, even after surgical resection. Hence, intensive postsurgical chemical therapies, such as the systemic delivery of various drugs and/or drug combinations, are typically followed after surgery. However, overcoming the blood-brain barrier by systemic administration to efficiently deliver drugs to the brain tumor remains a daunting goal. Therefore, various local drug delivery methods showing potential for improved therapeutic efficacy have been proposed. In particular, the recent application of electronic devices for the controlled delivery of chemotherapy drugs to GBM tissue has attracted attention. We herein review the recent progress of local drug delivery strategies, including electronics-assisted strategies, at the research and commercial level. We also present a brief discussion of the unsolved challenges and future research direction of localized chemotherapy methods for GBM.

Deep Learning-Based Prediction of the 3D Postorthodontic Facial Changes

With the increase of the adult orthodontic population, there is a need for an accurate and evidence-based prediction of the posttreatment face in 3 dimensions (3D). The objectives of this study are 1) to develop a 3D postorthodontic face prediction method based on a deep learning network using the patient-specific factors and orthodontic treatment conditions and 2) to validate the accuracy and clinical usability of the proposed method. Paired sets (n = 268) of pretreatment (T1) and posttreatment (T2) cone-beam computed tomography (CBCT) of adult patients were trained with a conditional generative adversarial network to generate 3D posttreatment facial data based on the patient's gender, age, and the changes of upper (ΔU1) and lower incisor position (ΔL1) as input. The accuracy was calculated with prediction error and mean absolute distances between real T2 (T2) and predicted T2 (PT2) near 6 perioral landmark regions, as well as percentage of prediction error less than 2 mm using test sets (n = 44). For qualitative evaluation, an online survey was conducted with experienced orthodontists as panels (n = 56). Overall, PT2 indicated similar 3D changes to the T2 face, with the most apparent changes simulated in the perioral regions. The mean prediction error was 1.2 ± 1.01 mm with 80.8% accuracy. More than 50% of the experienced orthodontists were unable to distinguish between real and predicted images. In this study, we proposed a valid 3D postorthodontic face prediction method by applying a deep learning algorithm trained with CBCT data sets.